LawnStarter

Director

• Own marketplace dynamics across pricing, Pro incentives, supply planning, matching, and Service Completion Rate
• Develop pricing strategies for instant quotes, manual quotes, dynamic pricing, and pricing tests
• Design and test Pro incentives in the field and build market-by-market, service-by-service supply planning tools
• Build forecasting, monitoring, and operating rhythms for seasonal supply and demand
• Partner with Product on matching and job distribution improvements
• Diagnose market constraints across pricing, incentives, planning, matching, and supply
• Build AI-first systems and agentic tools using LawnStarter's Brain toolkit
• Lead cross-functional efforts with Product, Growth, Finance, and Operations
• Own outcomes end to end, including recommendations, execution, experimentation, and measurement
• Report to the COO as an individual contributor

Director of Supply & Demand

Security Engineer

• Lead security end-to-end across the PHP/Laravel and TypeScript/React codebase, AWS infrastructure, payment and customer-data flows, and compliance posture
• Set the multi-year security direction and build scalable security controls
• Establish threat models, automated scanning, incident runbooks, and security standards
• Own application security, including secure SDLC, code and design reviews, CI scanning, and vulnerability-management processes
• Strengthen AWS and infrastructure security, including IAM, networking, encryption, secrets management, and EKS/Kubernetes hardening
• Map PCI scope and drive SOC 2 and LGPD readiness
• Manage vendor risk and respond to customer or auditor security questionnaires
• Improve detection coverage using Datadog, Sentry, and AWS signals
• Lead incident response and establish baseline MTTD/MTTR
• Define security controls, review gates, and conventions for AI-agent-authored code
• Create standards, playbooks, and hiring criteria for the future security team
• Collaborate with delivery teams and Cloud & DevOps while initially performing most hands-on work
• Build a business case and plan for the first security hires, with an explicit path to leading a small team in roughly 12–18 months

Lead Security Engineer

Data Analyst

• Own experimentation rigor across web funnels, SMS/drip, sales-driven tests, and SEO tests
• Design tests and make power, sample-size, significance, and readout-standard decisions
• Catch underpowered tests and false positives before they drive bad decisions
• Extend automated Growth metrics in Lightdash, Python-backed statistical-significance tooling, and Claude routines
• Build and maintain a trusted acquisition-to-conversion funnel across brands and paid, organic, and partner channels
• Provide CAC, LTV, and conversion-rate analyses for Growth investment prioritization
• Establish Year 1 standards and a playbook for scaling the Growth analytics function
• Recommend whether to buy or build the experimentation stack
• Partner directly with the Director of CRO, performance marketing, and Growth PMs
• Deliver analysis that drives quantified conversion lifts and push back when data contradicts popular ideas

Lead Data Analyst, Growth & Experimentation

QA Engineer

• Own and evolve the company-wide quality engineering roadmap
• Establish a quality accountability framework for engineers, engineering managers, and product managers
• Define test strategy across unit, component, contract, API, end-to-end, mobile, performance, load, and accessibility testing
• Develop and advance AI-native quality tooling for test generation, code review, and quality guidance
• Own CI/CD quality gates, E2E and visual regression pipelines, performance budgets, and mobile test automation across emulators and real device clouds
• Decide which checks block deployments and which run asynchronously while maintaining fast pipelines
• Connect monitoring to incident response and build actionable quality dashboards
• Provide technical mentorship to QA engineers across squads and provide input to their Engineering Managers for performance reviews
• Lead the QA squad's technical direction and continuously improve quality standards, tooling, and practices
• Report directly to the Head of Engineering

Principal Quality Engineer

• Deliver insights and reports that support product prioritization, assessments, and business recommendations
• Perform marketplace analyses ranging from A/B tests to multivariate models on retention or ETA
• Use advanced SQL and intermediate Python or R for modeling and analysis
• Create and maintain trusted, organized, actionable reporting systems and dashboards
• Build clean, documented datasets in the data warehouse using dbt
• Resolve conflicting metrics and establish trusted definitions
• Enable stakeholder self-service through reports, datasets, documentation, and training
• Identify decisions changed by analysis and drive stakeholders to act on findings
• Collaborate with product managers, researchers, and business stakeholders, primarily on the Pro/supply side of the marketplace
• Nurture a data-driven culture across the company

Lead Product Data Analyst

Full-stack Engineer

• Serve as the engineering anchor of a product initiative
• Shape problems and decide the technical approach
• Own architecture, data model, integrations, rollout plans, observability, and rollback strategies
• Direct AI agents to implement code and ensure production-ready quality
• Establish prompts, guardrails, evals, tests, and review loops
• Ship initiatives from problem framing through production and post-launch review
• Partner daily with the PM on scope and tradeoffs and with the designer on UX decisions and prototyping
• Collaborate with engineering peers and meet weekly with the Engineering Manager
• Own initiative metrics and present results 2–4 weeks after launch
• Ensure production correctness, security, performance, observability, and customer/pro experience
• Ship 3–4 initiatives in the first year and help at least two move their metrics
• Create reusable runbooks, evals, agent workflows, and post-launch write-ups

Staff Software Engineer – Product

Engineering Manager

• Lead cross-functional delivery teams of 4–5 engineers
• Report to the Head of Engineering
• Own delivery and people outcomes for the team
• Create concrete growth plans for every individual contributor
• Ensure the team ships on cadence with predictable quality
• Surface risks early, manage scope, and maintain commitments
• Address conflict directly and manage underperformance through a clear process
• Protect and develop top performers
• Partner with Product, Design, and other engineering teams
• Provide technical direction on system design and architectural risks
• Remove blockers without becoming overly involved in implementation
• Introduce AI tools and workflows for code review, documentation, sprint planning, and debugging
• Develop the next generation of senior engineers and tech leads
• Coordinate distributed sprint ceremonies and remote one-on-ones
• Orchestrate delivery from roadmap estimation through release
• Balance business value, technical soundness, security, architecture, and product speed
• Track AI adoption and improve measurable throughput without sacrificing quality
• Achieve year-one goals including documented growth plans within 60 days, committed-scope delivery within 90 days, retention-risk management, documented performance management, reliable commitments, and increased AI enablement

Software Engineering Manager

• Own and evolve the company-wide quality engineering roadmap
• Establish a quality accountability framework for engineers, EMs, and PMs
• Define test strategy across unit, component, contract, API, end-to-end, mobile, performance, load, and accessibility testing
• Develop and advance AI-native quality tooling, including AI agents and skills for test generation, code review, and quality guidance
• Own CI/CD and test infrastructure, including required checks, quality gates, E2E and visual regression pipelines, performance budgets, and mobile test automation
• Decide which checks block deployments and which run asynchronously while keeping pipelines fast
• Connect production monitoring to incident response and build actionable quality dashboards
• Provide technical mentorship to QA engineers across squads
• Collaborate with QA engineers and their Engineering Managers and provide input for performance reviews
• Improve quality standards, tooling, flakiness, pipeline cost, and synchronous versus asynchronous test execution
• Ship new AI-native quality capabilities and lead the QA squad's technical direction

Analytics Engineer

• Own the company-wide data roadmap for product, marketing, operations, and finance
• Prioritize platform health and stakeholder needs, present and defend the roadmap, and re-plan as the business changes
• Build automated monitoring for source data, pipelines, and reports
• Detect upstream schema and source changes before downstream breakage and run incidents to resolution
• Maintain data lineage and impact analysis from production source through warehouse models to dashboards
• Establish data contracts and integrate impact assessment into engineering change review
• Administer Lightdash, including workspace structure, permissions, certification, naming, rollout, enablement, query performance, and warehouse costs
• Extend and govern the semantic layer with one controlled definition per metric
• Govern Segment event tracking, including event review, catalog maintenance, naming, property dictionary, lifecycle, and drift detection
• Govern AI data access and keep the warehouse documented, consistent, safe, and AI-legible
• Manage data security and privacy, including access controls, PII handling, retention, and periodic access reviews
• Build documentation, ownership models, and review loops that make governance sustainable
• Start as an individual contributor and later hire and manage a Lead Analytics Engineer

Analytics Engineering Manager, Data Platform – Governance

• Serve as the engineering anchor for an initiative alongside a product manager, designer, and engineering peers
• Shape problems, determine technical approaches, direct AI agents, ship to production, and own outcomes
• Own architecture, data models, integrations, rollout plans, observability, and rollback strategies
• Establish prompts, guardrails, evals, tests, and review loops for safe, correct, production-ready agent-authored code
• Collaborate daily with the PM and designer, regularly with engineering peers, and weekly with the Engineering Manager
• Own initiative metrics and present post-launch results 2–4 weeks after launch
• Ensure production correctness, security, performance, observability, and customer and pro experience
• Ship 3–4 initiatives end-to-end in the first year and demonstrate measurable outcomes
• Create reusable runbooks, evals, agent workflows, and post-launch write-ups
• Work within LawnStarter's on-demand marketplace for lawn care and outdoor services expanding into home services

Staff Software Engineer, Product

• Deliver insights and reports that influence product prioritization, assessments, and business recommendations
• Partner with product managers, researchers, and business stakeholders
• Model and analyze marketplace data, including A/B tests and multivariate models
• Identify gaps, insights, optimizations, and drivers of key metrics
• Create and maintain trusted, organized, actionable reporting systems and dashboards
• Develop clean, documented datasets and models in the data warehouse using dbt
• Define, document, and standardize metrics across teams
• Enable stakeholder self-service through reports, datasets, documentation, and training
• Nurture a data-driven culture across the company
• Work primarily on the Pro/supply side of the marketplace

• Own the company-wide data roadmap and prioritize platform investments
• Build automated data quality and freshness monitoring across source data, pipelines, and reports
• Manage data lineage and impact analysis from production sources through warehouse models to dashboards
• Administer Lightdash, including workspace structure, permissions, certification, naming, rollout, enablement, query performance, and warehouse costs
• Extend and govern the semantic layer with one official definition per metric
• Govern Segment event tracking, including event standards, ownership, property dictionaries, review lifecycle, and drift detection
• Govern data access for AI tools and maintain an AI-legible warehouse
• Manage data security, PII handling, retention, and periodic access reviews under US state privacy laws
• Establish documentation, ownership models, and review loops for sustainable governance
• Lead incidents to resolution and connect production change review with downstream impact assessment
• Start as a solo contributor and later hire and manage a Lead Analytics Engineer